The Cost-Effectiveness of Mobile Health (mHealth) Interventions for Older Adults: Systematic Review

Improving post-concussion discharge education for families seeking emergency department care: intervention development

BACKGROUND

Pediatric emergency departments (ED) are where many families receive post-concussion medical care and thus an important context for helping parents build skills to support their child after discharge.

OBJECTIVE

Develop a strategy for increasing parent provision of emotional and instrumental support to their child after discharge and conduct a pilot test of this strategy's acceptability.

METHODS

In a large pediatric ED in the United States, we partnered with parents (n = 15) and clinicians (n = 15) to understand needs and constraints related to discharge education and to operationalize a strategy to feasibly address these needs. This produced a brief daily text message intervention for parents for 10 days post-discharge. We used a sequential cohort design to assess the acceptability this intervention and its efficacy in changing parenting practices in the 2-weeks post-discharge (n = 98 parents).

RESULTS

Parents who received the messaging intervention rated it as highly acceptable and had meaningfully higher scores for emotionally supportive communication with their child in the two weeks post-discharge than parents in the control condition (Cohen's d = 0.65, p = 0.021).

CONCLUSIONS

This brief messaging intervention is a promising strategy for enhancing discharge education post-concussion that warrants further evaluation.

Do medications increase the efficacy of digital interventions for smoking cessation? Secondary results from the iCanQuit randomized trial

BACKGROUND AND AIMS

iCanQuit is a smartphone application (app) proven efficacious for smoking cessation in a Phase III randomized controlled trial (RCT). This study aimed to measure whether medications approved by the US Food and Drug Administration (FDA) for smoking cessation would further enhance the efficacy of iCanQuit, relative to its parent trial comparator-the National Cancer Institute's (NCI's) QuitGuide app.

DESIGN

Secondary analysis of the entire parent trial sample of a two-group (iCanQuit and QuitGuide), stratified, doubled-blind RCT.

SETTING

United States.

PARTICIPANTS

Participants who reported using an FDA-approved cessation medication on their own (n = 619) and those who reported no use of cessation medications (n = 1469).

INTERVENTIONS

Participants were randomized to receive iCanQuit app or NCI's QuitGuide app.

MEASUREMENTS

Use of FDA-approved medications was measured at 3 months post-randomization. Smoking cessation outcomes were measured at 3, 6 and 12 months. The primary outcome was 12-month self-reported 30-day point prevalence abstinence (PPA).

FINDINGS

The data retention rate at the 12-month follow-up was 94.0%. Participants were aged 38.5 years, 71.0% female, 36.6% minority race/ethnicity, 40.6% high school or less education, residing in all 50 US States and smoking 19.2 cigarettes/day. The 29.6% of all participants who used medications were more likely to choose nicotine replacement therapy (NRT; 78.8%) than other cessation medications (i.e. varenicline or bupropion; 18.3 and 10.5%, respectively) and use did not differ by app treatment assignment (all P > 0.05). There was a significant (P = 0.049) interaction between medication use and app treatment assignment on PPA. Specifically, 12-month quit rates were 34% for iCanQuit versus 20% for QuitGuide [odds ratio (OR) = 2.36, 95% confidence interval (CI) = 1.59, 3.49] among participants reporting any medication use, whereas among participants reporting no medication use, quit rates were 28% for iCanQuit versus 22% for QuitGuide (OR = 1.41, 95% CI = 1.09, 1.82). Results were stronger for those using only NRT: 40% quit rates for iCanQuit versus 18% quit rates for QuitGuide (OR = 3.57, 95% CI = 2.20, 5.79).

CONCLUSIONS

The iCanQuit smartphone app for smoking cessation was more efficacious than the QuitGuide smartphone app, regardless of whether participants used medications to aid cessation. Smoking cessation medications, especially nicotine replacement therapy, might enhance the efficacy of the iCanQuit app.

A Proposal for a Robust Validated Weighted General Data Protection Regulation-Based Scale to Assess the Quality of Privacy Policies of Mobile Health Applications: An eDelphi Study

BACKGROUND

Health care services are undergoing a digital transformation in which the Participatory Health Informatics field has a key role. Within this field, studies aimed to assess the quality of digital tools, including mHealth apps, are conducted. Privacy is one dimension of the quality of an mHealth app. Privacy consists of several components, including organizational, technical, and legal safeguards. Within legal safeguards, giving transparent information to the users on how their data are handled is crucial. This information is usually disclosed to users through the privacy policy document. Assessing the quality of a privacy policy is a complex task and several scales supporting this process have been proposed in the literature. However, these scales are heterogeneous and even not very objective. In our previous study, we proposed a checklist of items guiding the assessment of the quality of an mHealth app privacy policy, based on the General Data Protection Regulation.

OBJECTIVE

To refine the robustness of our General Data Protection Regulation-based privacy scale to assess the quality of an mHealth app privacy policy, to identify new items, and to assign weights for every item in the scale.

METHODS

A two-round modified eDelphi study was conducted involving a privacy expert panel.

RESULTS

After the Delphi process, all the items in the scale were considered "important" or "very important" (4 and 5 in a 5-point Likert scale, respectively) by most of the experts. One of the original items was suggested to be reworded, while eight tentative items were suggested. Only two of them were finally added after Round 2. Eleven of the 16 items in the scale were considered "very important" (weight of 1), while the other 5 were considered "important" (weight of 0.5).

CONCLUSION

The Benjumea privacy scale is a new robust tool to assess the quality of an mHealth app privacy policy, providing a deeper and complementary analysis to other scales. Also, this robust scale provides a guideline for the development of high-quality privacy policies of mHealth apps.

How Could Sensor-Based Measurement of Physical Activity Be Used in Cardiovascular Healthcare?

Physical activity and cardiovascular disease (CVD) are intimately linked. Low levels of physical activity increase the risk of CVDs, including myocardial infarction and stroke. Conversely, when CVD develops, it often reduces the ability to be physically active. Despite these largely understood relationships, the objective measurement of physical activity is rarely performed in routine healthcare. The ability to use sensor-based approaches to accurately measure aspects of physical activity has the potential to improve many aspects of cardiovascular healthcare across the spectrum of healthcare, from prediction, prevention, diagnosis, and treatment to disease monitoring. This review discusses the potential of sensor-based measurement of physical activity to augment current cardiovascular healthcare. We highlight many factors that should be considered to maximise the benefit and reduce the risks of such an approach. Because the widespread use of such devices in society is already a reality, it is important that scientists, clinicians, and healthcare providers are aware of these considerations.

The Impact of a Digital Weight Loss Intervention on Health Care Resource Utilization and Costs Compared Between Users and Nonusers With Overweight and Obesity: Retrospective Analysis Study

BACKGROUND

The Noom Weight program is a smartphone-based weight management program that uses cognitive behavioral therapy techniques to motivate users to achieve weight loss through a comprehensive lifestyle intervention.

OBJECTIVE

This retrospective database analysis aimed to evaluate the impact of Noom Weight use on health care resource utilization (HRU) and health care costs among individuals with overweight and obesity.

METHODS

Electronic health record data, insurance claims data, and Noom Weight program data were used to conduct the analysis. The study included 43,047 Noom Weight users and 14,555 non-Noom Weight users aged between 18 and 80 years with a BMI of ≥25 kg/m² and residing in the United States. The index date was defined as the first day of a 3-month treatment window during which Noom Weight was used at least once per week on average. Inverse probability treatment weighting was used to balance sociodemographic covariates between the 2 cohorts. HRU and costs for inpatient visits, outpatient visits, telehealth visits, surgeries, and prescriptions were analyzed.

RESULTS

Within 12 months after the index date, Noom Weight users had less inpatient costs (mean difference [MD] -US $20.10, 95% CI -US $30.08 to -US $10.12), less outpatient costs (MD -US $124.33, 95% CI -US $159.76 to -US $88.89), less overall prescription costs (MD -US $313.82, 95% CI -US $565.42 to -US $62.21), and less overall health care costs (MD -US $450.39, 95% CI -US $706.28 to -US $194.50) per user than non-Noom Weight users. In terms of HRU, Noom Weight users had fewer inpatient visits (MD -0.03, 95% CI -0.04 to -0.03), fewer outpatient visits (MD -0.78, 95% CI -0.93 to -0.62), fewer surgeries (MD -0.01, 95% CI -0.01 to 0.00), and fewer prescriptions (MD -1.39, 95% CI -1.76 to -1.03) per user than non-Noom Weight users. Among a subset of individuals with 24-month follow-up data, Noom Weight users incurred lower overall prescription costs (MD -US $1139.52, 95% CI -US $1972.21 to -US $306.83) and lower overall health care costs (MD -US $1219.06, 95% CI -US $2061.56 to -US $376.55) per user than non-Noom Weight users. The key differences were associated with reduced prescription use.

CONCLUSIONS

Noom Weight use is associated with lower HRU and costs than non-Noom Weight use, with potential cost savings of up to US $1219.06 per user at 24 months after the index date. These findings suggest that Noom Weight could be a cost-effective weight management program for individuals with overweight and obesity. This study provides valuable evidence for health care providers and payers in evaluating the potential benefits of digital weight loss interventions such as Noom Weight.

mCrutch: A Novel m-Health Approach Supporting Continuity of Care

This paper reports the architecture of a low-cost smart crutches system for mobile health applications. The prototype is based on a set of sensorized crutches connected to a custom Android application. Crutches were instrumented with a 6-axis inertial measurement unit, a uniaxial load cell, WiFi connectivity, and a microcontroller for data collection and processing. Crutch orientation and applied force were calibrated with a motion capture system and a force platform. Data are processed and visualized in real-time on the Android smartphone and are stored on the local memory for further offline analysis. The prototype's architecture is reported along with the post-calibration accuracy for estimating crutch orientation (5° RMSE in dynamic conditions) and applied force (10 N RMSE). The system is a mobile-health platform enabling the design and development of real-time biofeedback applications and continuity of care scenarios, such as telemonitoring and telerehabilitation.

Health economic evaluation of preventive digital public health interventions using decision-analytic modelling: a systematized review

BACKGROUND

Digital public health (DiPH) provides novel approaches for prevention, potentially leading to long-term health benefits in resource-limited health systems. However, cost-effectiveness of DiPH interventions is unclear. This systematized review investigates the use of decision-analytic modelling in health economic evaluations of DiPH primary prevention and health promotion interventions, focusing on intervention's design, methods used, results, and reporting quality.

METHODS

PubMed, CINAHL, and Web of Science were searched for studies of decision-analytic economic evaluations of digital interventions in primary prevention or health promotion, published up to June 2022. Intervention characteristics and selected items were extracted based on the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). Incremental cost-effectiveness ratios (ICERs) were then extracted and price-adjusted to compare the economic evaluation results. Finally, the included studies' reporting quality was assessed by building a score using CHEERS.

RESULTS

The database search (including search update) produced 2,273 hits. After removing duplicates, 1,434 titles and abstracts were screened. Of the 89 studies meeting the full-text search criteria, 14 were ultimately reviewed. The most common targets were physical activity (five studies) and weight loss (four). Digital applications include text messages, web-based inventions, app-based interventions, e-learning devices, and the promotion of smartphone apps. The mean ICER of the 12 studies using quality-adjusted life years (QALYs) is €20,955 per QALY (min. - €3,949; max. €114,211). The mean of reported CHEERS items per study is 81% (min. 59%; max. 91%).

CONCLUSIONS

This review only includes primary prevention and health promotion, and thus excludes other DiPH fields (e.g. secondary prevention). It also focuses on decision-analytic models, excluding study-based economic evaluations. Standard methods of economic evaluation could be adapted more to the specifics of DiPH by measuring the effectiveness of more current technologies through alternative methods, incorporating a societal perspective, and more clearly defining comparators. Nevertheless, the review demonstrates using common thresholds that the new field of DiPH shows potential for cost-effective preventive interventions.

Cost-effectiveness of a direct to beneficiary mobile communication programme in improving reproductive and child health outcomes in India

INTRODUCTION

Kilkari is the largest maternal messaging programme of its kind globally. Between its initiation in 2012 in Bihar and its transition to the government in 2019, Kilkari was scaled to 13 states across India and reached over 10 million new and expectant mothers and their families. This study aims to determine the cost-effectiveness of exposure to Kilkari as compared with no exposure across 13 states in India.

METHODS

The study was conducted from a programme perspective using an analytic time horizon aligned with national scale-up efforts from December 2014 to April 2019. Economic costs were derived from the financial records of implementing partners. Data on incremental changes in the practice of reproductive maternal newborn and child health (RMNCH) outcomes were drawn from an individually randomised controlled trial in Madhya Pradesh and inputted into the Lives Saved Tool to yield estimates of maternal and child lives saved. One-way and probabilistic sensitivity analyses were carried out to assess uncertainty.

RESULTS

Inflation adjusted programme costs were US$8.4 million for the period of December 2014-April 2019, corresponding to an average cost of US$264 298 per year of implementation in each state. An estimated 13 842 lives were saved across 13 states, 96% among children and 4% among mothers. The cost per life saved ranged by year of implementation and with the addition of new states from US$392 ($385-$393) to US$953 ($889-$1092). Key drivers included call costs and incremental changes in coverage for key RMNCH practices.

CONCLUSION

Kilkari is highly cost-effective using a threshold of India's national gross domestic product of US$1998. Study findings provide important evidence on the cost-effectiveness of a national maternal messaging programme in India.

TRIAL REGISTRATION

NCT03576157.

Economic and Performance Evaluation of E-Health before and after the Pandemic Era: A Literature Review and Future Perspectives

E-Health represents one of the pillars of the modern healthcare system and a strategy involving the use of digital and telemedicine tools to provide assistance to an increasing number of patients, reducing, at the same time, healthcare costs. Measuring and understanding the economic value and performance of e-Health tools is, therefore, essential to understanding the outcome and best uses of such technologies. The aim of this paper is to determine the most frequently used methods for measuring the economic value and the performance of services in the framework of e-Health, considering different pathologies. An in-depth analysis of 20 recent articles, rigorously selected from more than 5000 contributions, underlines a great interest from the clinical community in economic and performance-related topics. Several diseases are the object of detailed clinical trials and protocols, leading to various economic outcomes, especially in the COVID-19 post-pandemic era. Many e-Health tools are mentioned in the studies, especially those that appear more frequently in people's lives outside of the clinical setting, such as apps and web portals, which allow for clinicians to keep in contact with their patients. While such e-Health tools and programs are increasingly studied from practical perspectives, such as in the case of Virtual Hospital frameworks, there is a lack of consensus regarding the recommended models to map and report their economic outcomes and performance. More investigations and guidelines by scientific societies are advised to understand the potential and path of such an evolving and promising phenomenon.

Can a Single Variable Predict Early Dropout From Digital Health Interventions? Comparison of Predictive Models From Two Large Randomized Trials

BACKGROUND

A single generalizable metric that accurately predicts early dropout from digital health interventions has the potential to readily inform intervention targets and treatment augmentations that could boost retention and intervention outcomes. We recently identified a type of early dropout from digital health interventions for smoking cessation, specifically, users who logged in during the first week of the intervention and had little to no activity thereafter. These users also had a substantially lower smoking cessation rate with our iCanQuit smoking cessation app compared with users who used the app for longer periods.

OBJECTIVE

This study aimed to explore whether log-in count data, using standard statistical methods, can precisely predict whether an individual will become an iCanQuit early dropout while validating the approach using other statistical methods and randomized trial data from 3 other digital interventions for smoking cessation (combined randomized N=4529).

METHODS

Standard logistic regression models were used to predict early dropouts for individuals receiving the iCanQuit smoking cessation intervention app, the National Cancer Institute QuitGuide smoking cessation intervention app, the WebQuit.org smoking cessation intervention website, and the Smokefree.gov smoking cessation intervention website. The main predictors were the number of times a participant logged in per day during the first 7 days following randomization. The area under the curve (AUC) assessed the performance of the logistic regression models, which were compared with decision trees, support vector machine, and neural network models. We also examined whether 13 baseline variables that included a variety of demographics (eg, race and ethnicity, gender, and age) and smoking characteristics (eg, use of e-cigarettes and confidence in being smoke free) might improve this prediction.

RESULTS

The AUC for each logistic regression model using only the first 7 days of log-in count variables was 0.94 (95% CI 0.90-0.97) for iCanQuit, 0.88 (95% CI 0.83-0.93) for QuitGuide, 0.85 (95% CI 0.80-0.88) for WebQuit.org, and 0.60 (95% CI 0.54-0.66) for Smokefree.gov. Replacing logistic regression models with more complex decision trees, support vector machines, or neural network models did not significantly increase the AUC, nor did including additional baseline variables as predictors. The sensitivity and specificity were generally good, and they were excellent for iCanQuit (ie, 0.91 and 0.85, respectively, at the 0.5 classification threshold).

CONCLUSIONS

Logistic regression models using only the first 7 days of log-in count data were generally good at predicting early dropouts. These models performed well when using simple, automated, and readily available log-in count data, whereas including self-reported baseline variables did not improve the prediction. The results will inform the early identification of people at risk of early dropout from digital health interventions with the goal of intervening further by providing them with augmented treatments to increase their retention and, ultimately, their intervention outcomes.

The Interaction of Perceived Susceptibility with Predictors of mHealth Technology Usage

Objectives: Based on the significance of mobile healthcare (mHealth) technologies in the dynamic and competitive environment, the current study examined the interactive effect of individuals' perceived susceptibility of being ill with the performance expectancy, social influence, and facilitating conditions on their intentions and actual usage of mHealth technologies during medical emergencies/disastrous situations. Methods: Surveying 417 mHealth users in Indonesia, the current study applied structural equational modeling in SmartPLS ver.4 software to analyze the collected data. Results: Results revealed the importance of individuals' expectations regarding the performance of mHealth technologies along with the views of their social networks and facilitating conditions provided by the healthcare organizations to influence mHealth technologies' use during medical emergencies. However, results showed the strongest influence of facilitating conditions on the usage of mHealth technologies reflecting the significance of ease of use. Additionally, findings showed the significant contingent role of the perceived susceptibility of users to augment their intentions to adopt the mHealth technologies. Conclusion: The current study is expected to bring valuable insights to health service providers, health professionals, medical institutions, and practitioners by highlighting factors that facilitate individuals to use mHealth technologies during emergencies.

The cost-effectiveness of digital health interventions: A systematic review of the literature

Background

Digital health interventions have significant potential to improve safety, efficacy, and quality of care, reducing waste in healthcare costs. Despite these premises, the evidence regarding cost and effectiveness of digital tools in health is scarce and limited.

Objectives

The aim of this systematic review is to summarize the evidence on the cost-effectiveness of digital health interventions and to assess whether the studies meet the established quality criteria.

Methods

We queried PubMed, Scopus and Web of Science databases for articles in English published from January 1, 2016 to December 31, 2020 that performed economic evaluations of digital health technologies. The methodological rigorousness of studies was assessed with the Consolidated Health Economic Evaluation Reporting Standards (CHEERS). The review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2009 checklist.

Results

Search identified 1,476 results, 552 of which were selected for abstract and 35 were included in this review. The studies were heterogeneous by country (mostly conducted in upper and upper-middle income countries), type of eHealth intervention, method of implementation, and reporting perspectives. The qualitative analysis identified the economic and effectiveness evaluation of six different types of interventions: (1) seventeen studies on new video-monitoring service systems; (2) five studies on text messaging interventions; (3) five studies on web platforms and digital health portals; (4) two studies on telephone support; (5) three studies on new mobile phone-based systems and applications; and (6) three studies on digital technologies and innovations.

Conclusion

Findings on cost-effectiveness of digital interventions showed a growing body of evidence and suggested a generally favorable effect in terms of costs and health outcomes. However, due to the heterogeneity across study methods, the comparison between interventions still remains difficult. Further research based on a standardized approach is needed in order to methodically analyze incremental cost-effectiveness ratios, costs, and health benefits.

Digital Technologies for Health Promotion and Disease Prevention in Older People: Protocol for a Scoping Review

BACKGROUND

Digital technologies could contribute to health promotion and disease prevention. It is unclear if and how such digital technologies address the health needs of older people in nonclinical settings (ie, daily life).

OBJECTIVE

This study aims to identify digital technologies for health promotion and disease prevention that target the needs of older people in nonclinical settings by performing a scoping review of the published literature. The scoping review is guided by the framework of Arksey and O'Malley.

METHODS

Our scoping review follows the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) guidelines. The information sources are bibliographic databases (MEDLINE, PsycINFO, CINAHL, and SCOPUS) and bibliographies of any included systematic reviews. Manual searches for additional studies will be performed in Google Scholar and most relevant journals. The electronic search strategy was developed in collaboration with a librarian who performed the search for studies on digital technologies for health promotion and disease prevention targeting the needs of older people. Study selection and data coding will be performed independently by 2 authors. Consensus will be reached by discussion. Eligibility is based on the PCC (Population, Concept, and Context) criteria as follows: (1) older people (population); (2) any digital (health) technology, such as websites, smartphone apps, or wearables (concept); and (3) health promotion and disease prevention in nonclinical (daily life, home, or community) settings (context). Primary studies with any design or reviews with a systematic methodology published in peer-reviewed academic journals will be included. Data items will address study designs, PCC criteria, benefits or barriers related to digital technology use by older people, and evidence gaps. Data will be synthesized using descriptive statistics or narratively described by identifying common themes. Quality appraisal will be performed for any included systematic reviews, using a validated instrument for this study type (A Measurement Tool to Assess Systematic Reviews, version 2 [AMSTAR2]).

RESULTS

Following preliminary literature searches to test and calibrate the search syntax, the electronic literature search was performed in March 2022 and manual searches were completed in June 2022. Study selection based on titles and abstracts was completed in July 2022, and the full-text screen was initiated in July 2022.

CONCLUSIONS

Our scoping review will identify the types of digital technologies, health targets in the context of health promotion and disease prevention, and health benefits or barriers associated with the use of such technologies for older people in nonclinical settings. This knowledge could guide further research on how digital technologies can support healthy aging.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/37729.

A Survey of Mobile Apps for the Care Management of Patients with Dementia

Objective: Dementia is a progressive neurocognitive disorder that currently affects approximately 50 million people globally and causes a heavy burden for their families and societies. This study analyzed mobile apps for dementia care in different languages and during the COVID-19 pandemic. Methods: We searched PubMed, Cochrane Collaboration Central Register of Con-trolled Clinical Trials, Cochrane Systematic Reviews, Google Play Store, Apple App Store, and Huawei App Store for mobile applications for dementia care. The Mobile Application Rating Scale (MARS) was used to assess the quality of applications. Results: We included 99 apps for dementia care. No significant difference in MARS scores was noted between the two language apps (Overall MARS: English: 3.576 ± 0.580, Chinese: 3.569 ± 0.746, p = 0.962). In the subscale analysis, English apps had higher scores of perceived impact than Chinese apps but these were not significant (2.654 ± 1.372 vs. 2.000 ± 1.057, p = 0.061). (2) Applications during the COVID-19 pandemic had higher MARS scores than those before the COVID-19 pandemic but these were not significant (during the COVID-19 pandemic: 3.722 ± 0.416; before: 3.699 ± 0.615, p = 0.299). In the sub-scale analysis, apps during the COVID-19 pandemic had higher scores of engagement than apps before the COVID-19 pandemic but these were not significant (3.117 ± 0.594 vs. 2.698 ± 0.716, p = 0.068). Conclusions: Our results revealed that there is a minor but nonsignificant difference between different languages and during the COVID-19 pandemic. Further cooperation among dementia professionals, technology experts, and caregivers is warranted to provide evidence-based and user-friendly information to meet the needs of users.

Short Term Economic Evaluation of the Digital Platform "Support, Monitoring and Reminder Technology for Mild Dementia" (SMART4MD) for People with Mild Cognitive Impairment and their Informal Caregivers

BACKGROUND

A randomized controlled trial of the SMART4MD tablet application was conducted for persons with mild cognitive impairment (PwMCI) and their informal caregivers to improve or maintain quality of life.

OBJECTIVE

The objective was to conduct economic evaluation of SMART4MD compared to standard care in Sweden from a healthcare provider perspective based on a 6-month follow-up period.

METHODS

Three hundred forty-five dyads were enrolled: 173 dyads in the intervention group and 172 in standard care. The primary outcome measures for PwMCI and informal caregivers were quality-adjusted life years (QALY). The results are presented as incremental cost-effectiveness ratios, and confidence intervals are calculated using non-parametric bootstrap procedure.

RESULTS

For PwMCI, the mean difference in total costs between intervention and standard care was € 12 (95% CI: -2090 to 2115) (US$ = € 1.19) and the mean QALY change was -0.004 (95% CI: -0.009 to 0.002). For informal caregivers, the cost difference was - € 539 (95% CI: -2624 to 1545) and 0.003 (95% CI: -0.002 to 0.008) for QALY. The difference in cost and QALY for PwMCI and informal caregivers combined was -€ 527 (95% CI: -3621 to 2568) and -0.001 (95% CI: -0.008 to 0.006). Although generally insignificant differences, this indicates that SMART4MD, compared to standard care was: 1) more costly and less effective for PwMCI, 2) less costly and more effective for informal caregivers, and 3) less costly and less effective for PwMCI and informal caregivers combined.

CONCLUSION

The cost-effectiveness of SMART4MD over 6 months is inconclusive, although the intervention might be more beneficial for informal caregivers than PwMCI.

A systematic review of economic evaluations of web-based or telephone-delivered interventions for preventing overweight and obesity and/or improving obesity-related behaviors

Obesity prevention interventions with behavioral or lifestyle-related components delivered via web-based or telephone technologies have been reported as comparatively low cost as compared with other intervention delivery modes, yet to date, no synthesized evidence of cost-effectiveness has been published. This study aimed to conduct a systematic review of economic evaluations of obesity prevention interventions with a telehealth or eHealth intervention component. A systematic search of six academic databases was conducted through October 2020. Studies were included if they reported full economic evaluations of interventions aimed at preventing overweight or obesity, or interventions aimed at improving obesity-related behaviors, with at least one intervention component delivered by telephone (telehealth) or web-based technology (eHealth). Findings were reported narratively, based on the Consolidated Health Economic Evaluation Reporting Standards. Twenty-seven economic evaluations were included from 20 studies meeting the inclusion criteria. Sixteen of the included interventions had a telehealth component, whereas 11 had an eHealth component. Seventeen interventions were evaluated using cost-utility analysis, five with cost-effectiveness analysis, and five undertook both cost-effectiveness and cost-utility analyses. Only eight cost-utility analyses reported that the intervention was cost-effective. Comparison of results from cost-effectiveness analyses was limited by heterogeneity in methods and outcome units reported. The evidence supporting the cost-effectiveness of interventions with a telehealth or eHealth delivery component is currently inconclusive. Although obesity prevention telehealth and eHealth interventions are gaining popularity, more evidence is required on their effectiveness and cost-effectiveness.

Next-Generation Implementation of Chimeric Antigen Receptor T-Cell Therapy Using Digital Health

Chimeric antigen receptor T-cell (CAR-T) therapy is a paradigm-shifting immunotherapy modality in oncology; however, unique toxicities such as cytokine release syndrome (CRS) and immune effector cell-associated neurotoxicity syndrome limit its ability to be implemented more widely in the outpatient setting or at smaller-volume centers. Three operational challenges with CAR-T therapy include the following: (1) the logistics of toxicity monitoring, ie, with frequent vital sign checks and neurologic assessments; (2) the specialized knowledge required for toxicity management, particularly with regard to CRS and immune effector cell-associated neurotoxicity syndrome; and (3) the need for high-quality symptomatic and supportive care during this intensive period. In this review, we explore potential niches for digital innovations that can improve the implementation of CAR-T therapy in each of these domains. These tools include patient-facing technologies and provider-facing platforms: for example, wearable devices and mobile health apps to screen for fevers and encephalopathy, electronic patient-reported outcome assessments-based workflows to assist with symptom management, machine learning algorithms to predict emerging CRS in real time, clinical decision support systems to assist with toxicity management, and digital coaching to help maintain wellness. Televisits, which have grown in prominence since the novel coronavirus pandemic, will continue to play a key role in the monitoring and management of CAR-T-related toxicities as well. Limitations of these strategies include the need to ensure care equity and stakeholder buy-in, both operationally and financially. Nevertheless, once developed and validated, the next-generation implementation of CAR-T therapy using these digital tools may improve both its safety and accessibility.